Detecting stock-price manipulation in an emerging market: The case of Turkey

Öğüt, Hulisi; Doğanay, M. Mete; Aktaş, Ramazan

doi:10.1016/j.eswa.2009.03.065

Cited by 92 publications

(43 citation statements)

References 17 publications

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“…Similar work has been carried out by firstly studying the reported manipulated cases and constructing a dataset of manipulated cases, and then modelling the returns, liquidity and volatility as well as the news and events related to the stocks during the manipulation period by linear and logistic regression [9]. Evaluations and comparisons of different techniques were also presented in [8] and [9], yet both works lack a reliable, reasonable analysis of the link between the abnormalities of the stock features and the disclosure of price manipulation. Therefore, this leaves a knowledge gap between the data attribute deviations and the detection techniques.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…The appropriateness of a sample entropy methodology as a measure for the detection was evaluated in [7]; however, the statistical results did not favour the properties of sample entropy as an indicator of price manipulation. Logistic regression with an artificial neural network and support vector machine has been studied and compared as a method of detecting trade based manipulation within the emerging Istanbul Stock Market [8]. The detection model was built based on the assumption of higher deviations of the statistical features of daily return, volume and volatilities from normal cases indicating manipulation.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudostationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.

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Section: Review Of Related Literaturementioning

confidence: 99%

Section: Review Of Related Literaturementioning

confidence: 99%

Detecting price manipulation in the financial market

Cao

Coleman

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques have also been applied for detection of manipulation. Based on trading data, some authors suggest that Artificial Neural Networks and Support Vector Machines are effective techniques to detect manipulation [13]. Others suggest that a method called "hidden Markov model with abnormal states" is capable to model and detect price manipulation patterns, but further analysis is necessary [14].…”

Section: Related Workmentioning

confidence: 99%

Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective

Martínez-Miranda

McBurney

Howard

2016

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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Abstract-Market manipulation is a strategy used by traders to alter the price of financial assets. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools have been developed to detect manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading from a macroscopic perspective of profit maximisation, two strategies that differ in the legal background but share the same elemental concept of market manipulation. We use a reinforcement learning framework within the full and partial observability of Markov decision processes and analyse the underlying behaviour of the manipulators by finding the causes of what encourages the traders to perform fraudulent activities. Procedures can be applied to counter the problem as our model predicts the activity of the manipulators.

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“…After manipulation process, the stock liquidity was increased. H. Ögüt, M. M. Doganay, and R. Aktas [5] studied trade-based manipulations. Stock price manipulation in Istanbul stock exchange, which is an emerging market, was investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Price Manipulation Detection Based on Mathematical Models

Leangarun¹,

Tangamchit²,

Thajchayapong³

2016

IJTEF

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Abstract-We investigated two popular scenarios of stock price manipulations: pump-and-dump and spoof trading. Pump-and-dump is a procedure to buy a stock and push its price up. Then, the manipulator dumps all of the stock he holds to make a profit. Spoof trading is a procedure to trick other investors that a stock should be bought or sold at the manipulated price. We proposed mathematical definitions based on level 2 data for both scenarios, and used them to generate a training set consisting of buy/sell orders in an order book of 10 depths. Order cancellations, which are important indicators for price manipulation, are also visible in this level 2 data. In this paper, we considered a challenging scenario where we attempted to use less-detailed level 1 data to detect manipulations even though using level 2 data is more accurate. First, we implemented feedforward neural network models that have level 1 data, containing less-detailed information (no information about order cancellation), but is more accessible to investors as an input. The neural network model achieved 88.28% accuracy for detecting pump-and-dump but it failed to model spoof trading effectively. Therefore, we further investigate the two-dimensional Gaussian model and show that it can detect spoof trading using level 2 data as input.Index Terms-Stock price manipulation, pump-and-dump, spoof trading, neural network. I. INTRODUCTIONStock market gathers participations from all kinds of investors. Millions of buy/sell orders enters the market every day. Stock price fluctuates due to several factors, mainly from the profit that the company can make. However, there are some investors who attempt to get benefits from the stock market using irregular trade behaviors that affect the stock price. Some of these attempts are illegal. The control of these irregular trade behaviors is difficult due to the large amount of trade data.Automatic computer algorithms for detecting price manipulation are the solution to this problem. It can scan large amount of price data and spot manipulations in a short time. Price manipulation can be divided into three categories: trade-based, information-based, and action-based. This research discusses a mathematical model that classifies trade-based price manipulations from normal trades in stock markets. Two types of manipulations are investigated: pump-and-dump and spoof trading. Pump-and-dump is an action of buying stock, making the price to go higher, and then selling to others for a profit. Spoof trading is an action of sending passive orders in large volume to trick others that the stock should be sold at that price. After the manipulators secure enough benefits from that artificial price, they cancel their passive orders. These actions allow the manipulators to sell their stock at a price higher than usual.The effectiveness of manipulation detection depends on how much the information we have. We rely on using the price data that buyers and sellers sent to the market. The trade data can be classified into two levels. Level 1...

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Detecting stock-price manipulation in an emerging market: The case of Turkey

Cited by 92 publications

References 17 publications

Detecting price manipulation in the financial market

Detecting price manipulation in the financial market

Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective

Stock Price Manipulation Detection Based on Mathematical Models

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